Big Data services for you from Tokyo

Based on years of software engineering and scientific data analysis, we offer you Big Data and predictive analytics solutions using a wide range of mathematical, software and scientific methods using tools including Mathematica, R, and many others.

Business collect and store increasing amounts of data from customers, and from sensors in equipment and machines. In many cases these data can be analyzed using mathematical methods and the efficiency of business operations can be improved.

A very large arsenal of mathematical tools is available to perform mathematical data analysis. To select the most appropriate mathematical tools for a particular task, it is necessary to understand the mathematical methods, as well as the application environment, which may include business factors and physics in the case of machinery and equipment.

Data analysis and predictive analysis

Telecommunications companies, insurance companies, and other subscription based businesses use data analysis to predict future customer behavior and to reduce churn. Retailers use data analysis to determine prices and to predict sales so that the right goods can be stocked on shelves at the most suitable prices.

Manufacturers use data analysis to determined the timing of maintenance and repairs, and they are myriads of other applications where data analysis can help to reduce churn, and more effective use of resources.

A mathematical model is trained by sets of “training samples”, e.g. past customer behavior or past measured data, then is tested with subsets of past data. Once the chosen model has been tested successfully it can be used to predict customer behavior, maintenance schedules or tomorrow’s weather.

What is the meaning of “Big Data”?

Dramatic decrease of the cost of storage enables the storage of large amounts of data. Marketing automation, internet data analysis, sensors, “The Internet of Things”, generates more and more data.

We apply mathematical models to these data to gain insights to make business more efficient, or to make machinery and equipment run more efficiently. Mathematical tools developed for sets with a few hundred data points in many cases are far too slow for data sets with millions or billions of data.

If we apply a mathematical model to a data set with N data points, some models will take take twice the time for a data set with 2N points, in such cases we say that the model scales linearly. Other models may need N^2 = N * N as much time, and for other models the time may scale with the third or even higher powers. Such processes will be inefficient or impossible to use for large sets of data.

Therefore, when we deal with “Big Data”, with big data sets with millions and more data, we often need different tools and methods than this commonly used for small data sets.

Methods and tools: numerical analysis

In data analysis we use a very wide array of mathematical tools depending on the problem we want to solve. Common models are linear regression or machine learning, but there are many many more. Each data analysis project will include a project section were different methods are selected and tested.